Few-Shot Link Prediction for Temporal Knowledge Graphs

This repository contains the implementation of the TFSC architectures described in the paper.

Install Pytorch (>= 1.1.0)

pip install pytorch

Python 3.x (tested on Python 3.6)

pip install python 3.6

Numpy

pip install numpy

Pandas

pip install pandas

tqdm

pip install tqdm

run the code:

python train.py --parameters

To run our code, we need to divide the data set according to the data set preprocess file first, or divide it according to our own needs. If we want to get the best results, we need to use Complex to pre-train and then embed it into the model.## Baselines

We use the following public codes for baselines and hyperparameters.

We implemented DistMult refer to [RotatE](: https://github.com/DeepGraphLearning/ KnowledgeGraphEmbedding.). The user can run the baselines by the following command.

cd ./baselines
bash run.sh train MODEL_NAME DATA_NAME 0 0 512 1024 512 200.0 0.0005 10000 8 0